Apparatus and method for estimating blood pressure

ABSTRACT

An apparatus for estimating blood pressure is provided. The apparatus for estimating blood pressure may include: a bio-signal measurer configured to measure a bio-signal from a user; and a processor configured to extract one or more feature values from the bio-signal, to adjust a combination coefficient for combining the one or more feature values based on a reference value associated with vascular compliance, and to estimate blood pressure based on the adjusted combination coefficient, and the one or more feature values extracted from the bio-signal.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This is a continuation application of U.S. application Ser. No.16/409,128 filed on May 10, 2019, which claims priority from KoreanPatent Application No. 10-2018-0118825, filed on Oct. 5, 2018, in theKorean Intellectual Property Office, the disclosures of which areincorporated herein by reference in their entireties.

BACKGROUND 1. Field

Example embodiments of the present disclosure relate to an apparatus andmethod for cufflessly estimating blood pressure.

2. Description of the Related Art

Recently, with an aging population, soaring medical costs, and a lack ofmedical personnel for specialized medical services, research is beingactively conducted on IT-medical convergence technologies, in which ITtechnology and medical technology are combined. Particularly, monitoringof the health condition of the human body is not limited to places suchas hospitals, but is expanding to mobile healthcare fields that maymonitor a user's health state anywhere and anytime in daily life at homeor office. Typical examples of bio-signals, which indicate the healthcondition of individuals, include an electrocardiography (ECG) signal, aphotoplethysmogram (PPG) signal, an electromyography (EMG) signal, andthe like, and various bio-signal sensors are being developed to measurethese signals in daily life. Particularly, a PPG sensor may estimateblood pressure of a human body by analyzing a shape of pulse waves whichreflect a cardiovascular state and the like.

SUMMARY

According to an aspect of an example embodiment, there is provided anapparatus for estimating blood pressure, the apparatus including: abio-signal measurer configured to measure a bio-signal from a user; anda processor configured to extract one or more feature values from thebio-signal, adjust a combination coefficient for combining the one ormore feature values based on a reference value associated with vascularcompliance, and estimate a blood pressure based on the adjustedcombination coefficient and the one or more feature values extractedfrom the bio-signal.

The reference value may be obtained at a reference time including atleast one of a calibration time of the bio-signal and a time at whichthe user is in a stable state.

The processor may be further configured to obtain, as the referencevalue, a maximum amplitude value of a reference bio-signal that ismeasured from the user at a reference time.

The processor may be further configured to: obtain a referencebio-signal including a first pulse waveform component and a second pulsewaveform component, from the user at a reference time; and obtain, asthe reference value, at least one of a ratio between a first amplitudevalue of a first pulse waveform component and a second amplitude valueof a second pulse waveform component of the reference bio-signal, and aratio between the first amplitude value and a maximum amplitude value ofthe reference bio-signal.

The reference value may include a vascular compliance index including anAugmentation Index.

The processor may be further configured to receive the vascularcompliance index from an external device which measures the vascularcompliance of the user.

The processor may be further configured to obtain the Augmentation Indexby analyzing a waveform of a reference bio-signal that is measured fromthe user at reference time.

The processor may be further configured to obtain an adjustment valuefor adjusting the combination coefficient by inputting the referencevalue into a pre-defined adjustment value estimation equation.

The processor may be further configured to obtain the adjustment valueby further inputting a statistical value of a plurality of referencevalues obtained from a plurality of other users, into the adjustmentvalue estimation equation.

The one or more feature values may include a first feature valueassociated with cardiac output and a second feature value associatedwith total peripheral resistance.

The processor may be further configured to obtain at least oneinformation of heart rate information, an area under the waveform of thebio-signal, time and amplitude values of a maximum point of thebio-signal, time and amplitude values of a minimum point of thebio-signal, and amplitude and time values of pulse waveform componentsincluded in the bio-signal, and extract the one or more feature valuesbased on the at least one information.

The processor may be further configured to obtain a first variation inthe first feature value compared to a first reference feature value, anda second variation in the second feature value compared to a secondreference feature value, obtain a third variation based on the firstvariation and second variation, and estimate the blood pressure based onthe first variation, the second variation, and the third variation.

The combination coefficient may be one of a plurality of combinationcoefficients. The processor may be further configured to adjust theplurality of combination coefficients corresponding to each of thefirst, the second, and the third variations, and combine the first, thesecond, and the third variations by multiplying the first, the second,and the third variations by the adjusted corresponding combinationcoefficients, respectively. The processor may be further configured toestimate the blood pressure by applying a scaling factor to a resultingvalue of the combining the first, the second, and the third variations.

In accordance with an aspect of an example embodiment, there is provideda method of estimating blood pressure, the method including: measuring abio-signal from a user; extracting one or more feature values from thebio-signal; adjusting a combination coefficient for combining the one ormore feature values based on a reference value associated with vascularcompliance; and estimating blood pressure based on the adjustedcombination coefficient, and the one or more feature values extractedfrom the bio-signal.

The method may further include: obtaining, as the reference value, amaximum amplitude value of a reference bio-signal that is measured fromthe user at reference time.

The method may further include: obtaining a reference bio-signalincluding a first pulse waveform component and a second pulse waveformcomponent, from the user at a reference time; and obtaining, as thereference value, at least one of a ratio between a first amplitude valueof a first pulse waveform component and a second amplitude value of asecond pulse waveform component of the reference bio-signal, and a ratiobetween the first amplitude value and a maximum amplitude value of thereference bio-signal.

The adjusting the combination coefficient may include obtaining anadjustment value for adjusting the combination coefficient by inputtingthe reference value into a pre-defined adjustment value estimationequation.

The adjusting the combination coefficient may include obtaining theadjustment value by further inputting a statistical value of a pluralityof values obtained from a plurality of other users, into the adjustmentvalue estimation equation.

The one or more feature values may include a first feature valueassociated with cardiac output and a second feature value associatedwith total peripheral resistance.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain embodiments, with reference to the accompanying drawings, inwhich:

FIG. 1 is a block diagram illustrating an apparatus for estimating bloodpressure according to an example embodiment;

FIG. 2 is a block diagram illustrating an apparatus for estimating bloodpressure according to another example embodiment;

FIGS. 3A, 3B, 3C, 3D, 3E, 3F, and 3G are diagrams explaining an exampleof estimating blood pressure;

FIG. 4 is a flowchart illustrating a method of estimating blood pressureaccording to an example embodiment;

FIG. 5 is a diagram illustrating a wearable device according to anexample embodiment; and

FIG. 6 is a diagram illustrating a smart device according to an exampleembodiment.

DETAILED DESCRIPTION

Example embodiments are described in greater detail below with referenceto the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exampleembodiments. However, it is apparent that the example embodiments can bepracticed without those specifically defined matters. Also, well-knownfunctions or constructions are not described in detail since they wouldobscure the description with unnecessary detail.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. Any references to singular may include pluralunless expressly stated otherwise. In addition, unless explicitlydescribed to the contrary, an expression such as “comprising” or“including” will be understood to imply the inclusion of stated elementsbut not the exclusion of any other elements. Also, the terms, such as‘part’ or ‘module’, etc., should be understood as a unit that performsat least one function or operation and that may be embodied as hardware,software, or a combination thereof.

Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list. For example, the expression, “at leastone of a, b, and c,” should be understood as including only a, only b,only c, both a and b, both a and c, both b and c, all of a, b, and c, orany variations of the aforementioned examples.

Hereinafter, example embodiments of an apparatus and method forestimating blood pressure will be described in detail with reference tothe accompanying drawings. The blood pressure estimating apparatusaccording to one or more example embodiments may be embedded in aterminal, such as a smartphone, a tablet PC, a desktop computer, alaptop computer, and the like, or may be manufactured as an independenthardware device. In this case, the independent hardware device may be awearable device worn on an object, and examples thereof include awristwatch-type wearable device, a bracelet-type wearable device, awristband-type wearable device, a ring-type wearable device, aglasses-type wearable device, a hairband-type wearable device, or thelike, but is not limited thereto.

FIG. 1 is a block diagram illustrating an apparatus for estimating bloodpressure according to an example embodiment. FIG. 2 is a block diagramillustrating an apparatus for estimating blood pressure according toanother example embodiment. FIGS. 3A to 3G are diagrams explaining anexample of estimating blood pressure.

Referring to FIGS. 1 and 2 , the blood pressure estimating apparatuses100 and 200 include a bio-signal measurer 110 and a processor 120.

The bio-signal measurer 110 includes one or more sensors, and maymeasure various bio-signals from an object using the sensors. Inparticular, the one or more sensors may be sensors for measuring aphotoplethysmogram (PPG) signal, an electrocardiography (ECG) signal, anelectromyography (EMG) signal, a ballistocardiogram (BCG) signal, andthe like, but are not limited thereto. Examples of the bio-signalmeasurer 110 may include an optical sensor, a spectrometer, a PPGsensor, an ECG sensor, an EMG sensor, and a BCG sensor.

The object may be a body part which comes into contact with or isadjacent to the bio-signal measurer 110, and may be a body part wherepulse waves may be easily measured. For example, the object may be anarea of skin on the wrist that is adjacent to the radial artery or ahuman skin area through which veins or capillaries pass. However, theobject is not limited thereto, and ray be peripheral body portions, suchas fingers, toes, and the like, which have a high density of bloodvessels.

The bio-signal measurer 110 may include a light source which emits lightonto an object, and a detector which detects light scattered orreflected from the object. The light source may include a light emittingdiode (LED), a laser diode, a fluorescent body, and the like, and may beformed as one or two or more ways. The detector lay include one pixel ora pixel array including two or more pixels, each of which may include aphoto diode, a photo transistor, an image sensor, and the like whichdetect light and convert the detected light signal into an electricsignal, but the detector is not limited thereto.

The processor 120 may be electrically connected to the bio-signalmeasurer 110. In response to a request for estimating blood pressure,the processor 120 may control the bio-signal measurer 110, and mayreceive a bio-signal from the bio-signal measurer 110. The request forestimating blood pressure may be input by a user or may be generated atpredetermined intervals. Upon receiving an electrical bio-signal fromthe bio-signal measurer 110, the processor 120 may perform apreprocessing process, such as filtering for removing noise, amplifyinga bio-signal, convening a bio-signal into a digital signal, and thelike.

A variation in Mean Arterial Pressure (MAP) may be proportional tocardiac output (CO) and total peripheral resistance (TPR), asrepresented by the following Equation 1.ΔMAP=CO×TPR  [Equation 1]

Herein, ΔMAP denotes a difference in MAP between the left ventricle andthe right atrium, in which MAP of the right atrium is generally in arange of 3 mmHg to 5 mmHg, such that MAP of the right atrium is similarto MAP of the left ventricle or MAP of the upper arm. Under a conditionwhere absolute actual CO and TPR values are known, MAP may be obtainedfrom the aorta or the upper arm. However, it may be difficult toestimate absolute CO and TPR values based on a bio-signal.

In an example embodiment, the processor 120 may extract a feature valueassociated with cardiac output (CO) (hereinafter referred to as a “firstfeature value”) from a bio-signal, and may extract a feature valueassociated with total peripheral resistance (TPR) (hereinafter referredto as a “second feature value”) from the bio-signal. Here, the firstfeature value may be a feature value which shows anincreasing/decreasing trend in proportion to an actual CO value whichrelatively increases/decreases when an actual TPR value does not changesignificantly compared to a reference TPR value that is measured whilethe user is in a stable state. Further, the second feature value may bea feature value which shows an increasing/decreasing trend in proportionto an actual TPR value which relatively increases/decreases when anactual/measured CO value does not change significantly compared to areference CO value that is measured while the user is in a stable state.

For example, the processor 120 may extract the feature values byanalyzing a waveform of the measured bio-signal. The processor 120 mayobtain, from a bio-signal, feature values such as heart rateinformation, time and amplitude values of a maximum point of thebio-signal, time and amplitude values of a minimum point of thebio-signal, an area under a bio-signal waveform, amplitude and timevalues of pulse waveform components included in the bio-signal,information on an internally dividing point between the obtained values,and may extract feature values by using the obtained characteristicpoints. In this case, the pulse waveform components included in thebio-signal may be obtained by performing secondary differentiation onthe bio-signal, and by detecting a local minimum point of the secondarydifferential signal.

Referring to FIG. 3A, the pulse wave signal PS is represented as asuperposition of propagation waves, starting from the heart toward thedistal end portions of the body or a branching point in the bloodvessel, and reflection waves returning back from the distal end portionsor the branching point in the blood vessel. FIG. 3A illustrates anexample where a waveform of a pulse wave signal PS is formed by asuperposition of five pulse waveform components (e.g., the propagationwave fw and the reflection waves rw1, rw2, rw3, and rw4).

The processor 120 may extract feature values from the pulse wave signalPS by analyzing the pulse waveform components fw, rw1, rw2, rw3, andrw4. For example, the processor 120 may use pulses up to the thirdconstituent pulse to estimate blood pressure. Pulses after the thirdpulse may not be observed depending on individuals in some cases, andmay be difficult to be found due to noise or have a low correlation withestimation of blood pressure.

For example, the processor 120 may extract, as characteristic points,times t₁, t₂, and t₃, and amplitudes P₁, P₂, and P₃ of a maximum pointof the first to the third constituent pulse waveforms fw, rw1, and rw2.In this case, upon obtaining the pulse wave signal PS, the processor 120may perform secondary differentiation on the obtained pulse wave signalPS, and may extract the times T₁, T₂, and T₃, and the amplitudes P₁, P₂,and P₃ of the maximum point of the first to the third constituent pulsewaveforms fw, rw1, and rw2 by using the secondary differential signal.For example, by detecting a local minimum point from the secondarydifferential signal, the processor 120 may extract the times T₁, T₂, andT₃ corresponding to the local minimum point of the first to the thirdconstituent pulse waveforms fw, rw1, and rw2, and may extract amplitudesP₁, P₂, and P₃ corresponding to the times T₁, T₂, and T₃ from the pulsewave signal PS. Here, the local minimum point may refer to the smallestvalue of the secondary differential signal in a given interval (e.g., asystolic interval, or a diastolic interval), and may correspond to aspecific point having a downward convex shape in the interval of thesecondary differential signal which is observed to be decreased and thenis increased again past the specific point. However, the characteristicpoint is not limited thereto, and the processor 120 may detect a localmaximum point from the secondary differential signal, and may extracttime and amplitude values corresponding to the detected local maximumpoint as characteristic points. The local maximum point may refer to thelargest value of the secondary differential signal in a given interval(e.g., a systolic interval, or a diastolic interval), and may correspondto a specific point having an upward convex shape in the interval whichis observed to be increased until the specific point and then isdecreased past the specific point.

In another example, the processor 120 may obtain, as characteristicpoints, a time t_(max) and an amplitude P_(max), at which an amplitudehas a maximum value in a specific interval of the pulse wave signal PS.In this case, the specific interval may refer to an interval between astarting point of the pulse wave signal PS and a point where a dicroticnotch (DN) appears, which indicates a systolic interval of bloodpressure.

In yet another example, the processor 120 may obtain, as characteristicpoints, a duration PPG_(dur) indicating the entire measurement timeperiod of a bio-signal, or an area PPG_(area) of a bio-signal waveform.In this case, the area under the bio-signal waveform may refer to theentire area under a bio-signal waveform, or an area under a bio-signalwaveform corresponding to a predetermined percentage (e.g., 70%) of thetotal duration PPG_(dur) of the bio-signal.

In still another example, the processor 120 may extract, as anadditional characteristic point, an internally dividing point betweenthe extracted two or more characteristic points. When an unstablewaveform is generated in a pulse wave signal due to an abnormalenvironment such as motion noise, sleep, and the like, characteristicpoints may be extracted at wrong locations. The measurement of bloodpressure may be supplemented by using the internally dividing pointbetween the wrongly extracted characteristic points.

For example, upon extracting characteristic points (T₁, P₁) and(T_(max), P_(max)) in the systolic interval, the processor 120 maycalculate an internally dividing point (T_(sys), P_(sys)) between theextracted characteristic points (T₁, P₁) and (T_(max), P_(max)). In thiscase, the processor 120 may apply a weighted value to time values T₁ andT_(max) of the two characteristic points (T₁, P₁) and (T_(max),P_(max)), and may calculate a time T_(sys) of the internally dividingpoint by using each of the time values to which the weighted value isapplied, and may extract an amplitude P_(sys) corresponding to the timeT_(sys) of the internally dividing point. However, the internallydividing point is not limited thereto, and by analyzing the waveform ofthe obtained bio-signal, the processor 120 may further calculate aninternally dividing point between the characteristic points (T₁, P₁) and(T₂, P₂) associated with the first and the second constituent pulsewaveforms fw and rw₁ in the systolic interval of blood pressure, and aninternally dividing point between the characteristic points (T₃, P₃) and(T₄, P₄) associated with the third and the fourth constituent pulsewaveforms rw₂ and rw₃ in a diastolic interval of blood pressure, and thelike.

The processor 120 may extract the first feature value and the secondfeature value by combining various characteristic points obtained fromthe bio-signal. For example, the processor 120 may extract the firstfeature value and the second feature value by multiplying, dividing,adding, and subtracting the plurality of characteristic points, or acombination thereof. Alternatively, the processor 120 may extract thefirst feature value and the second feature value by using a functionhaving, as input values, values obtained by multiplying, dividing,adding, and subtracting the plurality of characteristic points or acombination thereof. Here, the function may be a linear function, aquadratic function, a polynomial function, a logarithmic function, or anexponential function, but is not limited thereto, and any other type offunction may also be used. In yet another example, the processor 120 mayextract the first feature value and the second feature value by using afunction having at least one characteristic point as an input value, butis not limited thereto.

In addition, the processor 120 may extract the first feature value andthe second feature value by combining characteristic points differently.Further, by combining characteristic points differently according toblood pressure values to be extracted, e.g., mean arterial pressure,systolic blood pressure, diastolic blood pressure, processor 120 mayextract each characteristic point for each type of blood pressuremeasurements.

The processor 120 may calculate a first variation in the first featurevalue compared to a first reference feature value. Further, theprocessor 120 may calculate a second variation in the second featurevalue compared to a second reference feature value. For example, thefirst variation may increase as a ratio of the first feature value tothe first reference value increases, and the second variation mayincrease as a ratio of the second feature value to the second referencevalue increases. In this case, the first reference feature value and thesecond reference feature value may refer to feature values associatedwith CO and TPR which are extracted from the bio-signal acquired at areference time (e.g., a time of calibration or a time of a stable stateincluding a time of an empty stomach state). For example, the processor120 may calculate the first variation and the second variation by usingthe following Equation 2.

$\begin{matrix}{{{\Delta\; f_{1}} = {\frac{f_{1\;{our}}}{f_{1\;{cal}}} - 1}}{{\Delta\; f_{2}} = {\frac{f_{2\;{our}}}{f_{2\;{cal}}} - 1}}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$

Herein, Δf₁ denotes the first variation, f_(1cur) denotes the firstfeature value, f_(1cal) denotes the first reference feature value, Δf₂denotes the second variation, f_(2cur) denotes the second feature value,and f_(2cal) denotes the second reference feature value.

In addition, upon calculating the first variation and the secondvariation, the processor 120 may calculate a third variation based onthe first variation and the second variation. For example, the processor120 may calculate the third variation by multiplying the first variationby the second variation. In particular, the third variation may be afactor for correcting a blood pressure variation, which may not bereflected using only the first feature value and the second featurevalue under the condition of blood pressure change such ashigh-intensity aerobic exercise.

The processor 120 may estimate a blood pressure variation by combiningthe calculated first variation, second variation, and third variation.In particular, the processor 120 may multiply each of the first, thesecond, and the third variations by a combination coefficient, and mayadd up the multiplied variations. In this case, the processor 120 mayobtain a blood pressure estimation result, in which characteristics ofeach user are reflected, by further applying a scaling factor to theresult of combination. The following Equation 3 represents a bloodpressure estimation equation, but the equation is not limited thereto.ΔBP=SF(αΔƒ₁+βΔƒ₂+γΔƒ₃)  [Equation 3]

Herein, ΔBP denotes the estimated blood pressure variation; Δf₁, Δf₂,and Δf₃ each denote the first variation, the second variation, and thethird variation; α, β, and γ each denote combination coefficientsapplied to each of the variations, and may be defined according to thetypes of blood pressure to be estimated and/or characteristics of auser; and SF denotes a scaling factor pre-defined according tocharacteristics of a user and/or the types of blood pressure to beestimated. For example, the scaling factor may be a reference MAP, areference diastolic blood pressure (DBP), or a reference systolic bloodpressure (SBP_ at a calibration time which are measured from a user byan external blood pressure measuring apparatus, or may be a valuecalculated by combining two or more thereof.

Upon estimating the blood pressure variation as described above, theprocessor 120 may estimate blood pressure by using a function asrepresented by the following Equation 4.BP_(est)=BP_(cal)+ΔBP  [Equation 4]

Herein, BP_(est) denotes an estimated blood pressure value, ΔBP denotesan estimated blood pressure variation, and BP_(cal) denotes a referenceblood pressure at the time of calibration. In this case, blood pressureBP may refer to MAP, DBP, and SBP.

In an example embodiment, the processor 120 may independently estimatevariations in MAP, DBP, and SBP by using the following Equations 3 and4. For example, the first feature value and the second feature value maybe extracted separately for each type of blood pressure. Alternatively,the processor 120 may independently estimate variations in MAP, DBP, andSBP by setting a combination coefficient and/or a scaling factordifferently for each type of blood pressure.

In another example embodiment, the processor 120 may sequentiallyestimate MAP, DBP, and SBP. For example, the processor 120 may obtain anestimated MAP value by using the above Equations 3 and 4, and mayestimate DBP and SBP sequentially by using the estimated MAP value. Theprocessor 120 may estimate DBP and SBP by using a pulse pressure alongwith the estimated MAP value. In this case, the pulse pressure may beobtained by analyzing a bio-signal, a pulse pressure measured by a pulsepressure measuring device may be received, or a predetermined referencepulse pressure of a user may also be used. The following Equations 5 and6 are examples of functions for estimating DBP based on the estimatedMAP value and the pulse pressure.

$\begin{matrix}{{{DBP} = {{MAP} - \frac{PP}{3}}}{{DSP} = {{MAP} - {0.01 \times {\exp\left( {{{4.1}4} - \frac{40.74}{HR}} \right)} \times {PP}}}}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack \\{{SBP} = {{DBP} + {PP}}} & \left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack\end{matrix}$

Herein, MAP denotes mean arterial pressure, DBP denotes diastolic bloodpressure, SBP denotes systolic blood pressure, PP denotes pulsepressure, and HR denotes heart rate.

Generally, vascular compliance of each individual varies depending on acardiovascular health condition. When blood pressure changes or CO andTPR change in two blood vessels of different vascular compliances, ashape of pressure waves may be changed differently. When blood pressureis the same or when CO and TPR change according to vascular compliance,the waveform of PPG signal may be changed differently for eachindividual. As described above, when blood pressure is estimated byusing the first feature value and the second feature value which arebased on the waveform of a bio-signal, a variation in the first featurevalue and the second feature value may be different from a variation inthe actual CO and TPR values.

Referring to FIGS. 3B and 3C, in a blood pressure change mechanismassociated with high-intensity aerobic exercise, a heart rate HRincreases sharply in an interval of 2 to 4 of a measurement index. FIG.3B illustrates an example where in the change mechanism, an estimatedblood pressure value Est BP, which is estimated based on the firstfeature value and the second feature value, is measured lower than anactual blood pressure value Ref BP in the blood pressure changemechanism. Further, FIG. 3C illustrates an example where an estimatedblood pressure value Est BP is measured higher than the actual bloodpressure value Ref BP in the blood pressure change mechanism. Asillustrated therein, when blood pressure is estimated withoutconsidering vascular compliance of each individual, errors may occur,such as the estimated blood pressure value measured higher or lower thanthe actual blood pressure value, even in the same blood pressure changemechanism.

FIG. 3D illustrates a pulse wave signal in a condition showing theresult of FIG. 3B. Referring to FIG. 3D, an amplitude P1 of a firstpulse waveform component included in the pulse wave signal of areference time may be observed at a lower position than an amplitude P2of a second pulse waveform component. Then, referring to a pulse wavesignal at a measurement time after exercise, the amplitude of the firstpulse waveform component increases sharply. By contrast, FIG. 3Eillustrates a pulse wave signal in a condition showing the result ofFIG. 3C. Referring to FIG. 3E, the amplitude P1 of the first pulsewaveform component included in the pulse wave signal of the referencetime may be observed at a very high position. Then, referring to a pulsewave signal at a measurement time after exercise, it can be seen thatthe amplitude P1 of the first pulse waveform component hardly changes,but the amplitude P2 of the second pulse waveform component issignificantly decreased.

As described above, referring to FIGS. 3B and 3E, an amplitude of eachpulse waveform component included in the bio-signal of a time of astable state (e.g., an amplitude of the first pulse waveform component)may be different for each individual, and may be related to vascularcompliance of each individual. In the example embodiment, by reflectingthe effect of vascular compliance of each individual in estimation ofblood pressure using the first feature value and the second featurevalue, accuracy in estimating blood pressure may be improved.

For example, the processor 120 may obtain a reference value associatedwith vascular compliance, and may reflect an effect of vascularcompliance of each individual in each feature value by adjustingcombination coefficients α, β, and γ for each variation, which isdescribed above in Equation 3, based on the obtained reference value. Inthis case, only a portion of each combination coefficient may beadjusted, but the combination coefficient is not limited thereto. Forconvenience of explanation, an example of adjusting a combinationcoefficient of the second feature value associated with TPR (hereinafterreferred to as a “second combination coefficient”) will be describedbelow.

The processor 120 may obtain an adjustment value to adjust the secondcombination coefficient for the second feature value associated with TPRby using a pre-defined adjustment value estimation equation asrepresented by the following Equation 7. However, the adjustment valueis not limited thereto, and the processor 120 may obtain an adjustmentvalue to adjust a combination coefficient for another feature value byusing the following Equation 7 or other function similar thereto.

$\begin{matrix}{\delta = {{\lambda \times \left( {\frac{a}{b} - 1} \right)} + 1}} & \left\lbrack {{Equation}\mspace{14mu} 7} \right\rbrack\end{matrix}$

Here, δ denotes an adjustment value for adjusting the second combinationcoefficient; λ denotes a predetermined constant; a denotes a statisticalvalue (e.g., a mean value) of reference values related to vascularcompliance, which are obtained from a plurality of users, and may be avalue pre-obtained through preprocessing; and b denotes a referencevalue of a specific user desired to estimate blood pressure.

Upon obtaining the adjustment value, the processor 120 may multiply theobtained adjustment value δ by the second combination coefficient β, andmay combine variations by using the multiplied value (δ×β).

In addition, the processor 120 may obtain a reference value to be inputinto an adjustment value estimation equation at a reference time (e.g.,a time of a stable state including a time of an empty stomach state, ora time of calibration). For example, the processor 120 may calculate, asa reference value, a ratio between a first amplitude value at a positionof the first pulse waveform component and a second amplitude value at aposition of the second pulse waveform component, which are included in abio-signal of the reference time. In another example, the processor 120may calculate, as a reference value, a ratio between the first amplitudevalue and a maximum amplitude value of the bio-signal of the referencetime. Further, the processor 120 may calculate, as a reference value, aratio between the first amplitude value and an amplitude at a localmaximum point of a secondary differential signal of a bio-signal, anamplitude at a maximum point of a secondary differential signal, and thelike. However, the reference value is not limited thereto, and theprocessor 120 may use a ratio between the first amplitude value andvarious other values as a reference value.

In yet another example, the reference value may include a vascularcompliance index. The vascular compliance index may include anAugmentation Index (Aix). Here, the Aix refers to a value obtained bydividing a difference between a first systolic peak and a secondsystolic peak by a total peak, and it is known that the Aix reflectsvascular compliance. The processor 120 may receive the Aix at areference time from an external device which measures the Aix.Alternatively, the Aix may be measured similarly using pressure waves ofthe radial artery. In this case, the waveform of a pulse wave signal,which is measured from a body part such as a wrist, a finger, and thelike, is highly relevant to pressure waves of the artery, such that theprocessor 120 may obtain the Aix by analyzing the waveform of the pulsewave signal obtained at the reference time.

Referring to FIGS. 3F and 3G, an adjustment value calculation equationmay be obtained by calculating a correlation between an average oferrors, obtained from a plurality of users, and a reference value ofeach user. An average error value of each user may refer to an averagevalue of errors between blood pressure values actually measured at aplurality of measurement times by each user and blood pressure valuesestimated using the first feature value and the second feature value.Further, the reference value may be a pre-defined value to be used as aninput of the adjustment value calculation equation, and may refer toP1/P2, P1/Pmax, and the like as described above.

Referring back to FIG. 2 , the blood pressure estimating apparatus 200may further include a communication interface 210, an output interface220, and a memory 230.

The communication interface 210 may communicate with an external device250 by using wired or wireless communication techniques under thecontrol of the processor 120, and may transmit and receive various datato and from the external device 250. For example, the communicationinterface 210 may transmit a blood pressure estimation result to theexternal device 250, and may receive various criteria required forestimating blood pressure from the external device 250. For example, thecommunication interface 210 may receive a reference blood pressure whichis measured by a cuff-type blood pressure measuring device and the like,the vascular compliance index, the blood pressure estimation equation,the adjustment value estimation equation, and the like. In this case,examples of the external device 250 may include a cuff-type bloodpressure measuring device, a device for measuring vascular complianceindex, and an information processing device such as a smartphone, atablet PC, a desktop computer, a laptop computer, and the like.

Examples of the communication techniques may include Bluetoothcommunication, Bluetooth Low Energy (BLE) communication, Near FieldCommunication (NFC), WLAN communication, Zigbee communication, InfraredData Association (IrDA) communication, Wi-Fi Direct (WFD) communication,Ultra-Wideband (UWB) communication, Ant+ communication, WIFIcommunication, Radio Frequency Identification (RFID) communication, 3Gcommunication, 4G communication, 5G communication, and the like.However, this is merely exemplary and is not intended to be limiting.

The output interface 220 may output results processed by thebio-information measurer 110 and the processor 120. For example, theoutput interface 220 may visually output an estimated bio-informationvalue by using a display, or may output the information in a non-visualmanner through voice, vibrations, tactile sensation, and the like byusing a speaker, a haptic motor, a vibrator, and the like. The outputinterface 220 may divide a display area into two or more areas accordingto a setting, in which the output interface 220 may output a bio-signalgraph, a blood pressure estimation result, and the like, which are usedfor estimating blood pressure, in a first area; and may output a bloodpressure estimation history in the form of graphs in a second area. Ifan estimated blood pressure value falls outside a predetermined normalrange, the output interface 130 may output warning information invarious manners, such as highlighting an abnormal value in red and thelike, displaying the abnormal value along with a normal range,outputting a voice warning message, adjusting a vibration intensity, andthe like.

The memory 230 may store processing results of the bio-informationmeasurer 110 and the processor 120. Further, the memory 230 may storevarious criteria required for estimating blood pressure. For example,the criteria may include user feature information such as a user's age,gender, health condition, and the like. In addition, the criteria mayinclude various types of information, such as the reference bloodpressure, the blood pressure estimation equation, a blood pressureestimation interval, the adjustment value estimation equation, and thelike, but are not limited thereto.

The memory 230 may include at least one storage medium of a flash memorytype memory, a hard disk type memory, a multimedia card micro typememory, a card type memory (e.g., an SD memory, an XD memory, etc.), aRandom Access Memory (RAM), a Static Random Access Memory (SRAM), a ReadOnly Memory (ROM), an Electrically Erasable Programmable Read OnlyMemory (EEPROM), a Programmable Read Only Memory (PROM), a magneticmemory, a magnetic disk, and an optical disk, and the like, but is notlimited thereto.

FIG. 4 is a flowchart illustrating a method of estimating blood pressureaccording to an example embodiment. The method of estimating bloodpressure of FIG. 4 may be an example of a blood pressure estimatingmethod according to the example embodiment of FIG. 1 or 2 . Variousexample embodiments thereof are described above in detail, such that thedescription thereof will be briefly made below.

The blood pressure estimating apparatus 100/200 may receive a requestfor estimating blood pressure in operation 410. The blood pressureestimating apparatus 100/200 may provide an interface to a user, and mayreceive the request for estimating blood pressure input by the userthrough the interface. Alternatively, the blood pressure estimatingapparatus 100/200 may communicate with an external device 250, and mayreceive the request for estimating blood pressure from the externaldevice 250. In this case, the external device 250 may be a smartphone ora tablet PC carried by a user, and a user may control the operation ofthe blood pressure estimating apparatus 100/200 by using a device havingexcellent interface or computing performance.

Then, the blood pressure estimating apparatus 100/200 may obtain abio-signal from an object in operation 420. Examples of the bio-signalmay include various bio-signals, such as a PPG signal, an ECG signal, anEMG signal, a BCG signal, and the like.

Subsequently, the blood pressure estimating apparatus 100/200 mayextract feature values by analyzing the obtained bio-signal in operation430. In particular, the cardiovascular feature values may include afeature value associated with cardiac output and a feature valueassociated with total peripheral resistance. The blood pressureestimating apparatus 100/200 may extract feature values related tocardiac output and total peripheral resistance by analyzing abio-signal, a differential signal obtained by differentiating thebio-signal, and the like, and by properly combining heart rateinformation, time and amplitude values of a maximum point of thebio-signal, time and amplitude values of a minimum point of thebio-signal, an area under a waveform of the bio-signal, amplitude andtime values of pulse waveform components included in the bio-signal, andthe like.

The blood pressure estimating apparatus 100/200 may adjust a combinationcoefficient to combine the extracted feature values for estimating bloodpressure in operation 440. For example, the blood pressure estimatingapparatus 100/200 may obtain a reference value associated with vascularcompliance of a user based on a shape of a waveform of a bio-signalmeasured at a reference time, and may adjust a combination coefficientby using the reference value. For example, the blood pressure estimatingapparatus 100/200 may obtain, as a reference value, a ratio betweenpulse waveform components included in the bio-signal, and may calculatean adjustment value for adjusting a combination coefficient by inputtingthe obtained reference value into a pre-defined adjustment valuecalculation equation. The reference value is not limited thereto, andvarious other indices which may reflect vascular compliance of a usermay also be used.

The blood pressure estimating apparatus 100/200 may estimate bloodpressure by combining the feature values in operation 450. For example,the blood pressure estimating apparatus 100/200 may normalize eachfeature value by dividing each feature value by a corresponding featurevalue of a reference time, and may calculate a variation in each featurevalue based on the result of normalization. Further, upon calculatingthe variation in each feature value, the blood pressure estimatingapparatus 100/200 may estimate blood pressure by multiplying eachcalculated variation by a combination coefficient, or the adjustedcombination coefficient if adjusted in operation 440, and combining thevariations. In particular, the blood pressure estimating apparatus100/200 may estimate blood pressure by multiplying a value, obtained bycombining the variations, by a scaling factor.

FIG. 5 is a diagram illustrating a wearable device according to anexample embodiment. Various example embodiments of the blood pressureestimating apparatus 100/200 described above may be mounted in a smartwatch worn on a wrist or a smart band-type wearable device. However, thewearable device is not limited thereto, and may be mounted in asmartphone, a tablet PC, a desktop computer, a laptop computer, and thelike.

Referring to FIG. 5 , the wearable device 500 includes a main body 510and a strap 530.

The main body 510 may be formed to have various shapes, and may includemodules which are mounted inside or outside of the main body 510 toperform the aforementioned function of estimating blood pressure as wellas various other functions. A battery may be embedded in the main body510 or the strap 530 to supply power to various modules of the wearabledevice 500.

The strap 530 may be connected to the main body 510. The strap 530 maybe flexible, so as to be bent around a user's wrist. The strap 530 maybe bent in a manner that allows the strap 530 to be detached from theuser's wrist or may be formed as a band that is not detachable. Air maybe injected into the strap 530 or an airbag may be included in the strap530, so that the strap 530 may have elasticity according to a change inpressure applied to the wrist, and the change in pressure of the wristmay be transmitted to the main body 510.

The main body 510 may include a bio-signal measurer 520 for measuring abio-signal. The bio-signal measurer 520 may be mounted on a rear surfaceof the main body 510, which comes into contact with the upper portion ofa user's wrist, and may include a light source for emitting light ontothe skin of the wrist and a detector for detecting light scattered orreflected from the object. The bio-signal measurer 520 may beimplemented as a spectrometer including the light source and thedetector. The bio-signal measurer 520 may further include a contactpressure sensor for measuring contact pressure applied by the object.

A processor 120 may be mounted in the main body 510. The processor 120may be electrically connected to various modules, mounted in thewearable device 500, to control operations thereof.

In addition, the processor 120 may estimate blood pressure by using thebio-signal measured by the bio-signal measurer 520. The processor 120may obtain a feature value associated with CO and a feature valueassociated with TPR from the bio-signal. Further, the processor 120 maycalculate variations in the feature values by normalizing each featurevalue with a feature value of a reference time, and may estimate bloodpressure by combining the calculated variations. In particular, theprocessor 120 may obtain an additional feature value based on thefeature value associated with CO and the feature value associated withTPR. The processor 120 may estimate blood pressure by multiplying thevariations in each of the feature values by each combinationcoefficient, and combining the multiplied variations. In this case, inorder to properly compensate for a feature value, which is greatlyaffected by vascular compliance of each individual, the processor 120may obtain a reference value by analyzing a waveform of the bio-signalof the reference time, and may adjust a combination coefficient for avariation in the feature value, which is affected by vascularcompliance, by using the reference value, thereby reflecting the effectof vascular compliance of each individual in estimation of bloodpressure.

In the case where the processor 120 includes a contact pressure sensor,the processor 120 may monitor a contact state of the object based oncontact pressure between the wrist and the bio-signal measurer 520, andmay provide guidance on a contact position and/or a contact state to auser through a display.

Further, the main body 510 may include a memory 230 which stores aprocessing result of the processor 120 ions types of information. Inthis case, various types of information may include criteria forestimating blood pressure as well as information associated withfunctions of the wearable device 500.

In addition, the main body 510 may also include a manipulator 540 whichreceives a control command of a user and transmits the received controlcommand to the processor 120. The manipulator 540 may include a powerbutton to input a command to turn on/off the wearable device 500.

The display may be mounted on a front surface of the main body 510, andmay include a touch panel for touch input. The display may receive atouch input from a user, may transmit the received touch input to theprocessor 120, and may display a processing result of the processor 120.

For example, the display may display the estimated blood pressureinformation. In this case, the display may display additionalinformation, such as an estimation date of blood pressure, a user'shealth condition, and the like, along with the estimated blood pressureinformation. When a user requests detailed information by operating themanipulator 540 or by touching the display for touch input, the displaymay output detailed information in various manners.

Moreover, a communication interface, provided for communication with anexternal device such as a mobile terminal of a user, may be mounted inthe main body 510. The communication interface may transmit anestimation result of bio-information to an external device, e.g., auser's smartphone, to display the result to a user. However, this ismerely exemplary, and the communication interface may transmit andreceive various types of necessary information.

FIG. 6 is a diagram ting a smart device 600, to which embodiments of ablood pressure estimating apparatus are applied. In this case, the smartdevice may be a smartphone, a tablet PC, and the like.

Referring to FIG. 6 , the smart device 600 includes a main body 610 anda bio-signal measurer 630 mounted on one surface of a main body 610. Thebio-signal measurer 630 may include a pulse wave sensor which includesat least one or more light sources 631 and a detector 632. Asillustrated in FIG. 6 , the bio-signal measurer 630 may be mounted on arear surface of the main body 610, but is not limited thereto. Further,the bio-signal measurer 630 may be configured in combination with afingerprint sensor or a touch panel mounted on a front surface.

In addition, a display may be mounted on a front surface of the mainbody 610. The display may visually display an estimation result ofbio-information and the like. The display may include a touch panel, andmay receive various types of information input through the touch paneland transmit the received information to the processor 120.

Moreover, an image sensor 620 may be mounted in the main body 610. Whena user's finger approaches the bio-signal measurer 630 to measure apulse wave signal, the image sensor 620 may capture an image of thefinger and may transmit the captured image to the processor 120. Basedon the image of the finger, the processor 120 may identify a relativeposition of the finger with respect to an actual position of thebio-signal measurer 630, and may provide the relative position of thefinger to the user through the display, so as to guide measurement ofpulse wave signals with improved accuracy.

The processor 120 may estimate blood pressure by using the bio-signalmeasured by the bio-signal measurer 630. As described above, theprocessor 120 may extract feature values related to cardiac output andtotal peripheral resistance from the bio-signal, and may estimate bloodpressure by combining the extracted feature values. In this case, inorder to compensate for a feature value which is affected by vascularcompliance of each individual, the processor 120 may properly adjust acombination coefficient applied to the feature value, and then maycombine the feature values, and detailed description thereof is madeabove.

While not restricted thereto, an example embodiment can be embodied ascomputer-readable code on a computer-readable recording medium. Thecomputer-readable recording medium is any data storage device that canstore data that can be thereafter read by a computer system. Examples ofthe computer-readable recording medium include read-only memory (ROM),random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, andoptical data storage devices. The computer-readable recording medium canalso be distributed over network-coupled computer systems so that thecomputer-readable code is stored and executed in a distributed fashion.Also, an example embodiment may be written as a computer programtransmitted over a computer-readable transmission medium, such as acarrier wave, and received and implemented in general-use orspecial-purpose digital computers that execute the programs. Moreover,it is understood that in example embodiments, one or more units of theabove-described apparatuses and devices can include circuitry, aprocessor, a microprocessor, etc., and may execute a computer programstored in a computer-readable medium.

The foregoing exemplary embodiments are merely exemplary and are not tobe construed as limiting. The present teaching can be readily applied toother types of apparatuses. Also, the description of the exemplaryembodiments is intended to be illustrative, and not to limit the scopeof the claims, and many alternatives, modifications, and variations willbe apparent to those skilled in the art.

What is claimed is:
 1. An apparatus for estimating blood pressure, the apparatus comprising: a photoplethysmogram (PPG) sensor configured to measure a bio-signal from a user at a measurement time; and a processor configured to: extract, from the bio-signal, a cardiac output (CO) feature value and a total peripheral resistance (TPR) feature value, in response to determining that the user is in a non-stable state based on a heart rate change of the user that is monitored at a same time as the blood pressure is estimated, obtain a CO variation in the CO feature value between the measurement time and a reference time, a TPR variation in the TPR feature value between the measurement time and the reference time, and a product of the CO variation and the TPR variation, adaptively determine a combination coefficient for combining the CO variation, the TPR variation, and the product of the CO variation and the TPR variation, based on a reference value associated with vascular compliance, and estimate a blood pressure by combining the CO variation, the TPR variation, and the product of the CO variation and the TPR variation, using the combination coefficient.
 2. The apparatus of claim 1, wherein the reference time corresponds to a calibration time of the bio-signal or a time at which the user is in a stable state.
 3. The apparatus of claim 1, wherein the processor is further configured to obtain, as the reference value associated with vascular compliance, a maximum amplitude value of a reference bio-signal that is measured from the user at the reference time.
 4. The apparatus of claim 1, wherein the processor is further configured to: obtain a reference bio-signal including a first pulse waveform component and a second pulse waveform component, from the user at the reference time; and obtain, as the reference value, at least one of a ratio between a first amplitude value of the first pulse waveform component and a second amplitude value of the second pulse waveform component of the reference bio-signal, and a ratio between the first amplitude value and a maximum amplitude value of the reference bio-signal.
 5. The apparatus of claim 1, wherein the reference value comprises a vascular compliance index including an Augmentation Index.
 6. The apparatus of claim 5, wherein the apparatus further comprising a communication interface configured to receive the vascular compliance index from an external device which measures the vascular compliance of the user.
 7. The apparatus of claim 5, wherein the processor is further configured to obtain the Augmentation Index by analyzing a waveform of a reference bio-signal that is measured from the user at ftthe reference time.
 8. The apparatus of claim 1, wherein the processor is further configured to: obtain an adjustment value by inputting the reference value into a pre-defined adjustment value estimation equation, and determine the combination coefficient by adjusting a predefined combination coefficient based on the adjustment value.
 9. The apparatus of claim 8, wherein the processor is further configured to obtain the adjustment value by further inputting a statistical value of a plurality of reference values obtained from a plurality of other users, into the adjustment value estimation equation.
 10. The apparatus of claim 1, further comprising a user interface that allows the user to input user feature information that comprises a weight, a gender, and an age of the user, wherein the processor is further configured to estimate the blood pressure based on the user feature information, the adaptively determined combination coefficient, the CO variation and the TPR variation.
 11. A method of estimating blood pressure, the method comprising: measuring a bio-signal from a user at a measurement time; extracting, from the bio-signal, a cardiac output (CO) feature value and a total peripheral resistance (TPR) feature value; in response to determining that the user is in a non-stable state based on a heart rate change of the user that is monitored at a same time as the blood pressure is estimated, obtain a CO variation in the CO feature value between the measurement time and a reference time, a TPR variation in the TPR feature value between the measurement time and the reference time, and a product of the CO variation and the TPR variation; adaptively determine a combination coefficient for combining the CO variation, the TPR variation, and the product of the CO variation and the TPR variation, based on a reference value associated with vascular compliance; and estimating a blood pressure by combining the CO variation, the TPR variation, and the product of the CO variation and the TPR variation, using the combination coefficient.
 12. The method of claim 11, further comprising: obtaining, as the reference value, a maximum amplitude value of a reference bio-signal that is measured from the user at athe reference time.
 13. The method of claim 11, further comprising: obtaining a reference bio-signal including a first pulse waveform component and a second pulse waveform component, from the user at the reference time; and obtaining, as the reference value, at least one of a ratio between a first amplitude value of the first pulse waveform component and a second amplitude value of the second pulse waveform component of the reference bio-signal, and a ratio between the first amplitude value and a maximum amplitude value of the reference bio-signal.
 14. The method of claim 11, wherein the reference value comprises a vascular compliance index including an Augmentation Index.
 15. The method of claim 11, wherein the adaptively determining the combination coefficient comprises obtaining an adjustment value by inputting the reference value into a pre-defined adjustment value estimation equation, and determining the combination coefficient by adjusting a predefined combination coefficient based on the adjustment value.
 16. The method of claim 15, wherein the adaptively determining the combination coefficient comprises obtaining the adjustment value by further inputting a statistical value of a plurality of values obtained from a plurality of other users, into the adjustment value estimation equation. 